Clusters with random size: maximum likelihood versus weighted estimation

Abstract: There are many contemporary designs that do not use a random sample of a fixed, a priori determined size. In case of informative cluster sizes, the cluster size is influenced by the the cluster's data, but here we cope with some issues that even occur when the cluster size and the data are unrelated. First, fitting models to clusters of varying sizes is often more complicated than when all cluster have the same size. Secondly, in such cases, there usually is no so-called complete sufficient statistic

Data Splitting and Its Applications

status: publishe

Model Selection in Least Absolute Deviation Regression using ℓ1\ell_1-penalization

info:eu-repo/semantics/nonPublishe

A generalized quantile regression model

A new class of probability distributions, the so-called connected double truncated gamma distribution, is introduced. We show that using this class as the error distribution of a linear model leads to a generalized quantile regression model that combines desirable properties of both least squares and quantile regression methods: robustness to outliers and differentiable loss function.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A generalization of the traditional quantile regression and its penalized version

info:eu-repo/semantics/nonPublishe

On log-concavity of skew-symmetric distributions and their applications in penalized linear models

info:eu-repo/semantics/nonPublishe

Sparse Linear Inverse Problems in Presence of Outliers

info:eu-repo/semantics/nonPublishe

Solving Noisy ICA Using Multivariate Wavelet Denoising with an Application to Noisy Latent Variables Regression

Special Issue: Advances in Probability and StatisticsInternational audienceA novel approach to solve the independent component analysis (ICA) model in the presence of noise is proposed. We use wavelets as natural denoising tools to solve the noisy ICA model. To do this, we use a multivariate wavelet denoising algorithm allowing spatial and temporal dependency. We propose also using a statistical approach, named nested design of experiments, to select the parameters such as wavelet family and thresholding type. This technique helps us to select more convenient combination of the parameters. This approach could be extended to many other problems in which one needs to choose parameters between many choices. The performance of the proposed method is illustrated on the simulated data and promising results are obtained. Also, the suggested method applied in latent variables regression in the presence of noise on real data. The good results confirm the ability of multivariate wavelet denoising to solving noisy ICA